Data mining and statistical analysis enable users to build predictive models and discover hidden insights in their data. Predictive analysis encompasses a number of analytic techniques. For example, large quantities of data can be explored and analyzed, by automatic or semi-automatic means, to discover meaningful patterns and rules present in the analyzed data. Examples of predictions are focused on different challenges such as forecasting future performance, sales, and costs; definition of key influencers; trend determination in a business field; determination of existing relationships in the analyzed data; determination of existing anomalies; etc.
Organizations can gain business value by exploring transactional data typically generated within the enterprise or from unstructured data created by external sources (e.g. social media, historical records). Data used for analysis may be stored in data repositories or databases. For generating a data model based on data, an analysis is performed and an algorithm is applied over the data, which may be pulled out of the database. Once the data model is created, it may be used over new data to make predictions for future events. There are a number of algorithms that can be used when creating the data model: decision trees, regression, factor analysis, cluster analysis, time-series, neural nets, association rules, etc. Such algorithms are provided by different vendors and may be consumed in a data mining application for analysis. For example, the open-source statistical and data mining language and environment, the statistical programming language “R” provides data scientists with a lot of analytic possibilities. The introduction of “in-memory” technology has reduced the time and cost of data processing. The “in-memory” technology allows working with data stored in random access memory (RAM) for processing, without the traditional data retrieval from the database system. In such manner, predictive analysis can be performed against vast volumes of data in real time.